Prepend xts rows to a subset

Question

Supposing I need to apply an MA(5) to a batch of market data, stored in an xts object. I can easily pull the subset of data I wanted smoothed with xts subsetting:

x['2013-12-05 17:00:01/2013-12-06 17:00:00']

However, I need an additional 5 observations prior to the first one in my subset to "prime" the filter. Is there an easy way to do this?

The only thing I have been able to figure out is really ugly, with explicit row numbers (here using xts sample data):

require(xts)
data(sample_matrix)
x <- as.xts(sample_matrix)

x$rn <- row(x[,1])
frst <- first(x['2007-05-18'])$rn
finl <- last(x['2007-06-09'])$rn
ans <- x[(frst-5):finl,]

Can I just say bleah? Somebody help me.

UPDATE: by popular request, a short example that applies an MA(5) to the daily data in sample_matrix:

require(xts)
data(sample_matrix)
x <- as.xts(sample_matrix)$Close

calc_weights <- function(x) {
    ##replace rnorm with sophisticated analysis
    wgts <- matrix(rnorm(5,0,0.5), nrow=1)
    xts(wgts, index(last(x)))
}

smooth_days <- function(x, wgts) {
    w <- wgts[index(last(x))]
    out <- filter(x, w, sides=1)
    xts(out, index(x))
}

set.seed(1.23456789)
wgts <- apply.weekly(x, calc_weights)
lapply(split(x, f='weeks'), smooth_days, wgts)

For brevity, only the final week's output:

[[26]]
                [,1]
2007-06-25        NA
2007-06-26        NA
2007-06-27        NA
2007-06-28        NA
2007-06-29 -9.581503
2007-06-30 -9.581208

The NAs here are my problem. I want to recalculate my weights for each week of data, and apply those new weights to the upcoming week. Rinse, repeat. In real life, I replace the lapply with some ugly stuff with row indexes, but I'm sure there's a better way.

In an attempt to define the problem clearly, this appears to be a conflict between the desire to run an analysis on non-overlapping time periods (weeks, in this case) but requiring overlapping time periods of data (2 weeks, in this case) to perform the calculation.

Answer 1

Here's one way to do this using endpoints and a for loop. You could still use the which.i=TRUE suggestion in my comment, but integer subsetting is faster.

y <- x*NA                   # pre-allocate result
ep <- endpoints(x,"weeks")  # time points where parameters change

set.seed(1.23456789)
for(i in seq_along(ep)[-(1:2)]) {
  rng1 <- ep[i-1]:ep[i]          # obs to calc weights
  rng2 <- ep[i-2]:ep[i]          # "prime" obs
  wgts <- calc_weights(x[rng1])
  # calc smooth_days on rng2, but only keep rng1 results
  y[rng1] <- smooth_days(x[rng2], wgts)[index(x[rng1])]
}